The present invention generally relates to the field of networked computerized systems utilized to monitor, log, and display relevant manufacturing/production events and associated data. More particularly, the present invention relates to manufacturing execution systems (MES). Such systems generally execute above/outside of a control layer of a manufacturing/process control system to record production events and related event data.
Industry increasingly depends upon highly automated supervisory control and data acquisition (SCADA) systems to ensure that industrial processes are run efficiently, safely and reliably while lowering their overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and periodically report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example the measurements produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a tallied inventory of packages waiting in a shipping line, or a photograph of a room in a factory. Often sophisticated process management and control software examines the incoming data, produces status reports, and, in many cases, responds by sending commands to actuators/controllers that adjust the operation of at least a portion of the industrial process. The data produced by the sensors also allow an operator to perform a number of supervisory tasks including: tailor the process (e.g., specify new set points) in response to varying external conditions (including costs of raw materials), detect an inefficient/non-optimal operating condition and/or impending equipment failure, and take remedial actions such as move equipment into and out of service as required.
Typical industrial processes are extremely complex and receive substantially greater volumes of information than any human could possibly digest in its raw form. By way of example, it is not unheard of to have thousands of sensors and control elements (e.g., valve actuators) monitoring/controlling aspects of a multi-line process within an industrial plant. These sensors are of varied types and report on varied characteristics of the process. Their outputs are similarly varied in the meaning of their measurements, in the amount of data sent for each measurement, and in the frequency of their measurements. As regards the latter, for accuracy and to enable quick response, some of these sensors/control elements take one or more measurements every second. Multiplying a single sensor/control element by thousands of sensors/control elements (a typical industrial control environment) results in an overwhelming volume of data flowing into the manufacturing information and process control system. Sophisticated data management and process visualization techniques have been developed to handle the large volumes of data generated by such system.
Highly advanced human-machine interface/process visualization systems exist today that are linked to data sources such as the above-described sensors and controllers. Such systems acquire and digest (e.g., filter) the process data described above. The digested process data in-turn drives a graphical display rendered by a human machine interface. An example of such system is the well-known Wonderware INTOUCH® human-machine interface (HMI) software system for visualizing and controlling a wide variety of industrial processes. An INTOUCH HMI process visualization application includes a set of graphical views of a particular process. Each view, in turn, comprises one or more graphical elements. The graphical elements are “animated” in the sense that their display state changes over time in response to associated/linked data sources. For example, a view of a refining process potentially includes a tank graphical element. The tank graphical element has a visual indicator showing the level of a liquid contained within the tank, and the level indicator of the graphical element rises and falls in response to a stream of data supplied by a tank level sensor indicative of the liquid level within the tank. Animated graphical images driven by constantly changing process data values within data streams, of which the tank level indicator is only one example, are considerably easier for a human observer to comprehend than a stream of numbers. For this reason process visualization systems, such as INTOUCH, have become essential components of supervisory process control and manufacturing information systems.
The MES monitors production and records various production/manufacturing events and applies known business rules to render decisions governing production operations carried out by the SCADA system. MES systems interface to higher level enterprise resource planning (ERP) systems. The exemplary SCADA/MES environment schematically depicted in FIG. 1 comprises a SCADA portion 11 and an MES database 13. Runtime process data generated by a regulatory control system 12 is received by the SCADA portion 11 through any combination of process data interfaces 14. As those skilled in the art will appreciate, the source of the process data is generally sensor data provided by field devices to regulatory control processors (via a variety of interfaces). The process data thereafter passes from the regulatory control system 12 to the SCADA portion 10 via any of a variety of communications channels including gateways, integrators, and runtime process data storage applications (e.g., plant Historian database).
An MES application 15 running on a plant monitoring application node in the SCADA/MES environment schematically depicted in FIG. 1, provides a series of views driven by the production/utilization information contained within the configured entities (elements) within the MES database 13. MES applications software systems provide a configurable facility for tracking the status (and thus utilization and availability) of plant equipment. Thus, MES applications capture and provide real-time, instantaneous plant/production information relating to the operational status of equipment within manufacturing process chains. MES applications thus facilitate tracking equipment utilization and improving/optimizing plant equipment utilization through efficient use of the plant equipment.
Known MES applications provide performance monitoring on an entity basis. Entities are physical assets in a process/manufacturing facility (broadly, a “plant”) whose activity is to be tracked by the MES. Entities can be an entire plant, an area of a plant (e.g., a production floor or warehouse), an organizational group of machines (e.g., those in a particular department), a piece of equipment (e.g., a mixer, dispenser, palletizer, etc.), or a module that makes up a piece of equipment. Entities may operate in varying states (e.g., running, idle, down, maintenance, etc.) at different times. Some MES applications are configured to present information about the operational states of selected entities. Likewise, some MES applications are configured to present information about any known reasons for a presently existing operational state of an entity. Still other MES applications are configured to present information about the production of an entity.
To facilitate the display of relevant entity information, known MES applications provide configuration tools that allow users to create data models of entities, utilization states, and reasons. An MES application may include model configuration templates for the various configurable MES models. A user may respond to queries in a configuration tool to populate the fields of a model configuration template and instantiate a model of a particular entity, state, reason, etc. Thus, for example, a user may be presented with an entity configuration view which queries the user as to a new entity's name, capabilities, etc. Likewise, a user may be presented with a utilization configuration view that queries the user as to a new utilization state's name, associated reasons, etc. As discussed in U.S. Pat. No. 8,639,376, which is assigned to the assignee of the present application and is hereby for all purposes incorporated by reference, data models may be imported from unified SCADA systems into the MES application. In some systems, modeled entities may be related to modeled utilization states and reasons, for example, using relational database techniques.
MES applications receive data from the physical plant either by way of manual data entry from the plant floor or automated reporting systems. Using entity attributes stored in an entity model and logical scripts (broadly, “procedures”) stored in the entity database 13, the MES application can display information about the production/operation of a physical entity. If the entity model is associated with utilization states and reasons models, the MES application may be capable of displaying an indication of the utilization state of a particular physical entity and the reason for the present state. Thus, known MES applications are capable of providing information about the production and operation of individual entities. For example, an MES application may present information about the operational state of a particular piece of equipment in the process plant. Using stored procedures, the MES application may be able to determine the production rate of the particular piece of equipment, including information about the status of any jobs assigned to the equipment.
In modern plants, the entity-centric approach to MES reporting has proven inadequate. In many cases, plant operators are concerned with the production/operation of an arrangement of entities, which together carry out a particular production task (broadly, a “production line”). Known MES applications do not support modeling of production lines. For example, known model templates do not support the notion of an arrangement of entities, and instead treat each entity as its own self-contained model. While some systems provide configuration templates for entity models, configuring models of arrangements of entities requires the creation of custom scripts. Similarly, while graphical views are provided for presenting information about the production/operation of individual entities, these views are not aggregated to present information about the production/operation of a line of entities in known MES applications. Because known systems do not support modeling arrangements of entities, stored procedures may only provide production analytics on an entity-by-entity basis, bound by the constraints of the entity models. However, in some instances, line analytics may provide richer information. Accordingly, an MES application that provides configuration tools for configuring data models for lines of entities is desired. Moreover, an MES application with a line model template for modeling arrangements of entities is desired. Likewise, an MES application with analytical tools that evaluate the real-time or near real-time production of a physical production line is desired.